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arXiv:2602.15848v1 Announce Type: cross Abstract: This study validates Large Language Models (LLMs) as a dynamic alternative to questionnaire-based personality assessment. Using a within-subjects experiment (N=33), …
arXiv:2602.15849v1 Announce Type: cross Abstract: Peer review relies on substantive, evidence-based questions, yet existing LLM-based approaches often generate surface-level queries, drawing over 50\% of their …
arXiv:2602.15851v1 Announce Type: cross Abstract: Applications of narrative theories using large language models (LLMs) deliver promising use-cases in automatic story generation and understanding tasks. Our …
arXiv:2602.15852v1 Announce Type: cross Abstract: Clinical natural language processing (NLP) models have shown promise for supporting hospital discharge planning by leveraging narrative clinical documentation. However, …
arXiv:2602.15853v1 Announce Type: cross Abstract: We propose a lightweight explainable guardrail (LEG) method for the classification of unsafe prompts. LEG uses a multi-task learning architecture …
arXiv:2602.15854v1 Announce Type: cross Abstract: Large language models show potential in task-oriented dialogue systems, yet existing training methods often rely on token-level likelihood or preference …
arXiv:2602.15855v1 Announce Type: cross Abstract: Hybrid reasoning systems that combine learned components with model-based inference are increasingly deployed in tool-augmented decision loops, yet their runtime …
arXiv:2602.15856v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) effectively grounds Large Language Models (LLMs) with external knowledge and is widely applied to Web-related tasks. However, …
arXiv:2602.15858v1 Announce Type: cross Abstract: As large language models (LLMs) move from static reasoning tasks toward dynamic environments, their success depends on the ability to …
arXiv:2602.15861v1 Announce Type: cross Abstract: Text analysis of tabular data relies on two core operations: \emph{summarization} for corpus-level theme extraction and \emph{tagging} for row-level labeling. …
arXiv:2602.15862v1 Announce Type: cross Abstract: Recent advances in Multimodal Large Language Models (MLMMs) have enabled recipe generation from food images, yet outputs often contain semantically …
arXiv:2602.15863v1 Announce Type: cross Abstract: Recent studies have shown that Large Language Models (LLMs) can improve their reasoning performance through self-generated few-shot examples, achieving results …